Abstract:
Surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals even though they typically exhibit very different mean values and variability. At the global scale, in particular, it is currently impossible to determine which soil moisture climatology is more correct. The biases pose a severe obstacle to exploiting the useful information contained in satellite retrievals of soil moisture in a data assimilation algorithm. A simple method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data. Cdf estimation typically requires a long data record. By using spatial averaging with a 2 degree moving window we can obtain statistics based on a one-year satellite record that are a good approximation of the desired local statistics of a long time series. This key property opens up the possibility for operational use of current and future soil moisture satellite data.